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https://github.com/zebrajr/pytorch.git
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c78ab28441
Summary: This PR implements the necessary hooks/stubs/enums/etc for complete ONNX Runtime (ORT) Eager Mode integration. The actual extension will live out of tree at https://github.com/pytorch/ort. We have been [working on this at Microsoft](https://github.com/microsoft/onnxruntime-pytorch/tree/eager-ort/torch_onnxruntime) for the last few months, and are finally ready to contribute the PyTorch core changes upstream (nothing major or exciting, just the usual boilerplate for adding new backends). The ORT backend will allow us to ferry [almost] all torch ops into granular ONNX kernels that ORT will eagerly execute against any devices it supports (therefore, we only need a single ORT backend from a PyTorch perspective). Pull Request resolved: https://github.com/pytorch/pytorch/pull/58248 Reviewed By: astaff Differential Revision: D30344992 Pulled By: albanD fbshipit-source-id: 69082b32121246340d686e16653626114b7714b2
211 lines
8.0 KiB
Python
211 lines
8.0 KiB
Python
import os
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import unittest
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import torch.testing._internal.common_utils as common
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from torch.testing._internal.common_utils import IS_WINDOWS
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from torch.testing._internal.common_cuda import TEST_CUDA
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import torch
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import torch.backends.cudnn
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import torch.utils.cpp_extension
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try:
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import pytest
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HAS_PYTEST = True
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except ImportError as e:
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HAS_PYTEST = False
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# TODO: Rewrite these tests so that they can be collected via pytest without
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# using run_test.py
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try:
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if HAS_PYTEST:
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cpp_extension = pytest.importorskip("torch_test_cpp_extension.cpp")
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ort_extension = pytest.importorskip("torch_test_cpp_extension.ort")
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rng_extension = pytest.importorskip("torch_test_cpp_extension.rng")
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else:
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import torch_test_cpp_extension.cpp as cpp_extension
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import torch_test_cpp_extension.ort as ort_extension
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import torch_test_cpp_extension.rng as rng_extension
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except ImportError as e:
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raise RuntimeError(
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"test_cpp_extensions_aot.py cannot be invoked directly. Run "
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"`python run_test.py -i test_cpp_extensions_aot_ninja` instead."
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) from e
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class TestCppExtensionAOT(common.TestCase):
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"""Tests ahead-of-time cpp extensions
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NOTE: run_test.py's test_cpp_extensions_aot_ninja target
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also runs this test case, but with ninja enabled. If you are debugging
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a test failure here from the CI, check the logs for which target
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(test_cpp_extensions_aot_no_ninja vs test_cpp_extensions_aot_ninja)
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failed.
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"""
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def test_extension_function(self):
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x = torch.randn(4, 4)
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y = torch.randn(4, 4)
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z = cpp_extension.sigmoid_add(x, y)
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self.assertEqual(z, x.sigmoid() + y.sigmoid())
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def test_extension_module(self):
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mm = cpp_extension.MatrixMultiplier(4, 8)
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weights = torch.rand(8, 4, dtype=torch.double)
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expected = mm.get().mm(weights)
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result = mm.forward(weights)
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self.assertEqual(expected, result)
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def test_backward(self):
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mm = cpp_extension.MatrixMultiplier(4, 8)
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weights = torch.rand(8, 4, dtype=torch.double, requires_grad=True)
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result = mm.forward(weights)
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result.sum().backward()
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tensor = mm.get()
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expected_weights_grad = tensor.t().mm(torch.ones([4, 4], dtype=torch.double))
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self.assertEqual(weights.grad, expected_weights_grad)
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expected_tensor_grad = torch.ones([4, 4], dtype=torch.double).mm(weights.t())
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self.assertEqual(tensor.grad, expected_tensor_grad)
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@unittest.skipIf(not TEST_CUDA, "CUDA not found")
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def test_cuda_extension(self):
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import torch_test_cpp_extension.cuda as cuda_extension
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x = torch.zeros(100, device="cuda", dtype=torch.float32)
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y = torch.zeros(100, device="cuda", dtype=torch.float32)
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z = cuda_extension.sigmoid_add(x, y).cpu()
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# 2 * sigmoid(0) = 2 * 0.5 = 1
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self.assertEqual(z, torch.ones_like(z))
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@unittest.skipIf(IS_WINDOWS, "Not available on Windows")
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def test_no_python_abi_suffix_sets_the_correct_library_name(self):
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# For this test, run_test.py will call `python setup.py install` in the
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# cpp_extensions/no_python_abi_suffix_test folder, where the
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# `BuildExtension` class has a `no_python_abi_suffix` option set to
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# `True`. This *should* mean that on Python 3, the produced shared
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# library does not have an ABI suffix like
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# "cpython-37m-x86_64-linux-gnu" before the library suffix, e.g. "so".
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root = os.path.join("cpp_extensions", "no_python_abi_suffix_test", "build")
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matches = [f for _, _, fs in os.walk(root) for f in fs if f.endswith("so")]
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self.assertEqual(len(matches), 1, msg=str(matches))
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self.assertEqual(matches[0], "no_python_abi_suffix_test.so", msg=str(matches))
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def test_optional(self):
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has_value = cpp_extension.function_taking_optional(torch.ones(5))
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self.assertTrue(has_value)
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has_value = cpp_extension.function_taking_optional(None)
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self.assertFalse(has_value)
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class TestORTTensor(common.TestCase):
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def test_unregistered(self):
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a = torch.arange(0, 10, device='cpu')
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with self.assertRaisesRegex(RuntimeError, "Could not run"):
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b = torch.arange(0, 10, device='ort')
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def test_zeros(self):
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a = torch.empty(5, 5, device='cpu')
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self.assertEqual(a.device, torch.device('cpu'))
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b = torch.empty(5, 5, device='ort')
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self.assertEqual(b.device, torch.device('ort', 0))
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self.assertEqual(ort_extension.get_test_int(), 0)
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self.assertEqual(torch.get_default_dtype(), b.dtype)
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c = torch.empty((5, 5), dtype=torch.int64, device='ort')
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self.assertEqual(ort_extension.get_test_int(), 0)
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self.assertEqual(torch.int64, c.dtype)
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def test_add(self):
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a = torch.empty(5, 5, device='ort', requires_grad=True)
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self.assertEqual(ort_extension.get_test_int(), 0)
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b = torch.empty(5, 5, device='ort')
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self.assertEqual(ort_extension.get_test_int(), 0)
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c = a + b
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self.assertEqual(ort_extension.get_test_int(), 1)
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def test_conv_backend_override(self):
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# To simplify tests, we use 4d input here to avoid doing view4d( which
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# needs more overrides) in _convolution.
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input = torch.empty(2, 4, 10, 2, device='ort', requires_grad=True)
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weight = torch.empty(6, 4, 2, 2, device='ort', requires_grad=True)
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bias = torch.empty(6, device='ort')
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# Make sure forward is overriden
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out = torch.nn.functional.conv1d(input, weight, bias, 2, 0, 1, 1)
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self.assertEqual(ort_extension.get_test_int(), 2)
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self.assertEqual(out.shape[0], input.shape[0])
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self.assertEqual(out.shape[1], weight.shape[0])
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# Make sure backward is overriden
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# Double backward is dispatched to _convolution_double_backward.
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# It is not tested here as it involves more computation/overrides.
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grad = torch.autograd.grad(out, input, out, create_graph=True)
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self.assertEqual(ort_extension.get_test_int(), 3)
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self.assertEqual(grad[0].shape, input.shape)
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class TestRNGExtension(common.TestCase):
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def setUp(self):
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super(TestRNGExtension, self).setUp()
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def test_rng(self):
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fourty_two = torch.full((10,), 42, dtype=torch.int64)
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t = torch.empty(10, dtype=torch.int64).random_()
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self.assertNotEqual(t, fourty_two)
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gen = torch.Generator(device='cpu')
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t = torch.empty(10, dtype=torch.int64).random_(generator=gen)
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self.assertNotEqual(t, fourty_two)
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self.assertEqual(rng_extension.getInstanceCount(), 0)
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gen = rng_extension.createTestCPUGenerator(42)
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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copy = gen
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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self.assertEqual(gen, copy)
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copy2 = rng_extension.identity(copy)
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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self.assertEqual(gen, copy2)
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t = torch.empty(10, dtype=torch.int64).random_(generator=gen)
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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self.assertEqual(t, fourty_two)
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del gen
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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del copy
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self.assertEqual(rng_extension.getInstanceCount(), 1)
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del copy2
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self.assertEqual(rng_extension.getInstanceCount(), 0)
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@unittest.skipIf(not TEST_CUDA, "CUDA not found")
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class TestTorchLibrary(common.TestCase):
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def test_torch_library(self):
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import torch_test_cpp_extension.torch_library # noqa: F401
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def f(a: bool, b: bool):
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return torch.ops.torch_library.logical_and(a, b)
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self.assertTrue(f(True, True))
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self.assertFalse(f(True, False))
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self.assertFalse(f(False, True))
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self.assertFalse(f(False, False))
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s = torch.jit.script(f)
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self.assertTrue(s(True, True))
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self.assertFalse(s(True, False))
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self.assertFalse(s(False, True))
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self.assertFalse(s(False, False))
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self.assertIn('torch_library::logical_and', str(s.graph))
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if __name__ == "__main__":
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common.run_tests()
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